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Data
Warehousing in Telephone Networks
1998-2003
a market research report
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Data warehouses serve as powerful strategic weapons in the telecom marketing wars, arming carriers with information to identify the most profitable, highest-spending telecom customers. More powerful than any previous type of statistical analysis, data warehouse technology exposes customer usage patterns and spending habits. Until now, the maximum number of customer variables that could be evaluated at once was seven; in a data warehouse, the number of variables are essentially limitless.
Data warehouses can be simultaneously mined by marketing, operations, finance, and call center managers to test promotional campaigns, forecast demand for network development, target cross-selling efforts, detect fraud, and acquire, win-back, and retain customers. With such tremendous amounts of information being crunched, carriers can design campaigns to maximize the profitability and satisfaction of targeted customers, making true mass customization and one-to-one marketing possible.
According to Insight, the cost of building a data mart or data warehouse can run anywhere from $40,000 to $3 million to implement, depending on the size of the carrier and the number of subscribers involved. Much of the upfront cost is in the hardware infrastructure; software and operational costs will grow as the system grows. Insight forecasts that worldwide expenditures by telecom service providers on data warehouse systems and projects will grow from $284 million in 1998 to just over $2.18 billion in 2003. However, the economic benefits projected for a carrier can easily justify the data warehouse investment.
Data Warehousing in Telephone Networks examines data warehousing applications, reviews implementations by leading carriers, and includes thirty strategic profiles of data warehouse vendors. The study concludes with global, five-year forecasts of data warehousing revenue by application, software tools and servers, and type of carrier.
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Report Excerpt
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Warehouses as Weapons
Increasing competition, deregulation, and industry
convergence around the globe is accelerating the pace of
investment by telecommunications carriers in data
warehouses. Though data warehouse technology has been
around for 20 years, the spectacular investment Insight
is predicting in this technology reflects the maturity of
data warehouse solutions in general, and the emergence of
prepackaged solutions for the telecom segment that make
supply-side economics attractive. Carriers desperate to
find the means to create competitive differentiation in
what are basically commodity products are driving the
demand for tools which will help them understand and
predict their customers' preferences and behavior.
Data warehouses are strategic weapons in the telecom
marketing wars because they help carriers identify the
customers who are worth targeting so that marketing
resources are not wasted on unprofitable customers.
Before the use of warehouses, carriers invested in
advertising, telemarketing, direct mail, and other
promotions, blindly targeting their entire customer base.
A data warehouse can tell carriers the distinguishing
characteristics of their most profitable and
highest-spending customers and provide them with a list
of these customers to target. Sprint found that the top
15 percent of their customers accounted for 50 percent of
their business and that the bottom 20 percent erased that
profit. With this knowledge, Sprint could design
marketing campaigns to retain and expand upon the top 15
percent while encouraging the bottom 20 percent to
migrate.
The Arsenal
Data warehouses are data storage, retrieval, and analysis
engines. Because they can work effectively with
multi-terabytes of records, they surpass the capability
of traditional tools to perform statistical analysis.
Data warehouses use neural network technology and other
advanced mathematical and statistical analysis techniques
to analyze data on a multidimensional basis. Data marts
are smaller data warehouses that contain a subset of all
the data and are designed to be relevant to a particular
function or department, such as marketing, finance, or
network operations.
Today the inputs for most carrier data warehouses come
from call detail records (CDRs), customer billing
records, and demographics purchased from third parties.
These inputs track all types of customer usage and
details about calling patterns. In a typical data
warehouse, information from a variety of systems is
extracted, transformed, and cleansed, and business rules
are developed to help clarify and standardize the data.
The most significant task in building a data warehouse is
moving data from numerous legacy (or pre-existing)
systems into the data warehouse and ensuring that the
data is consistent.
All told, carrier applications fall into five categories:
- Customer/market analyses,
- Product promotional analyses,
- Network operations,
- Market modeling analyses, and
- Fraud control.
Sales and marketing applications, including churn
analysis and customer retention, are the data warehouse
applications most in demand and are often implemented
first. These applications provide the quickest return on
investment and address the biggest problems that carriers
face in today's competitive market: customer retention,
acquisition, and win back. Most carriers start with
customer profiling, marketing, sales, and churn
applications and later expand the use of their data
warehouse systems into other areas, such as network
operations and finance.
One or Many Data Warehouses
The time and expense required to bring up a full
corporate-wide warehouse has engendered an alternative
solution. Proponents of data warehouses and data marts
are debating the benefits of using a separate data
warehouse for each department of a company. The
alternative is to use a central enterprise-wide data
warehouse with separate departmental data marts connected
to, but operating independently from, the larger
warehouse. If the company chooses to implement separate
data warehouses for different departments, they will
develop in isolation. The smaller warehouses will not be
able to receive simultaneous feeds from a single data
warehouse source, nor can they be accessed
interchangeably by users. As a result, they will develop
in a stovepipe rather than an integrated fashion.
Companies that pursue a distributed data mart strategy
instead can build separate data marts for each distinct
subject area or user group. This approach is less
expensive initially, is easier to implement, and requires
no consensus among user groups. The separate data marts
must all link into one original data warehouse, however,
so there is one single version of "the truth,"
as NCR phrases it, from which multiple data marts can be
built.
Business Case for Data Warehouses
It is not difficult for an average carrier with 500,000
customers to justify the multimillion-dollar investments
in data warehouse technology. The average churn rate of
20-25 percent implies that 125,000 customers will leave
this carrier each year. Because of the cost of flyers,
telemarketing, direct mail, and TV commercials, the
average acquisition cost per customer is $300. It costs
$50-$60 a year to retain a customer, while the average
customer value is $500 a year. Data warehouses have
proven to be capable of reducing churn by a conservative
estimate of five percent for carriers. A reduction of
that size in churn will keep 6,250 customers from leaving
the service, thus resulting in $3.1 million (6,250 times
$500) retained revenue. On the average, the direct saving
will be $1.9 million per year (6,250 times the $300
acquisition cost).
The cost of building a data mart or a data warehouse can
run anywhere from $40,000 to $3 million, depending on the
size of the carrier and the number of subscribers
involved. Much of the upfront cost is in the hardware
infrastructure; software and operational costs will
increase as the system grows. The economic benefits
projected for a hypothetical carrier easily justify such
an investment.
Data warehouses may be even more important than the
current investment logic implies. Carriers have had a
fixed view of the world for the most part, based
legitimately on 100 years of success. This worldview
equates a customer to a billed telephone number and
focuses on analyzing usage of telephone numbers, rather
than usage by individual customers. In the brave new
world of deregulation, however, telephone companies will
be entering new businesses, such as cable,
video-on-demand, and interactive services. In this
context, the data warehouse is akin to a powerful
strategic weapon that can be re-targeted depending on how
the telecom evolves over the next several years.
The Data Warehouse Market
Insight forecasts that worldwide expenditures by
telecommunications service providers on data warehouse
systems and projects will grow from $284 million in 1998
to just over $2,180 million in 2003. Included in these
expenditures are all hardware, software, and services
directly related to implementing corporate data
warehouses and local data marts. It does not include the
business management consulting or the process engineering
required to realize benefits from the analyses of the
data.
Additional market forecasts for data warehouse systems,
data mart systems, data mining software, and OLAP
software in North America, Europe/Middle East/Africa,
Asia/Pacific, and Latin America/Caribbean are found in
the full Insight report “Data Warehousing in
Telephone Networks 1998-2003”. Order your copy
today.
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Market Segmentation
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- Product Type
- Data Warehouse Systems
- Data Mart Systems
- Data Mining Software
- OLAP Software
- Market Segment
- ILECs
- IXCs
- CLECs
- Wireless
- US MSOs
- US ISPs
- International Wireline
- International Wireless
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Table of Contents
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Chapter I
EXECUTIVE SUMMARY
1.1 Warehouses as Weapons
1.2 The Arsenal
1.2.1 One or Many Data Warehouses
1.3 Business Case for Data Warehouses
1.4 The Data Warehouse Market
Chapter II
DATA WAREHOUSE TECHNOLOGY OVERVIEW
2.1 Data Warehouse Implementation
2.1.1 Hardware
2.1.2 The Database
2.1.2.1 Data Acquisition
2.1.2.2. Data Storage
2.1.2.3 Meta Data
2.1.2.4 Data Access Tools
2.1.2.5 Data Mining
2.1.2.6 Data Delivery
2.2 Updating and Managing the Data Warehouse
2.2.1 Business Rules
2.3 The Great Data Warehouse/Data Mart Debate
2.4 Carrier Requirements for Data Warehouse Technology
2.4.1 Difference in Use Between Wireless and Wireline
Carriers
2.5 Data Warehouse Implementation Process
Chapter III
DATA WAREHOUSE APPLICATIONS FOR CARRIERS
3.1 The Uses of the Data Warehouse
3.1.1 Fraud Control
3.1.2 Network Operations
3.1.3 Call Center Access
3.2 The Business Case for a Data Warehouse
3.2.1 The Right Approach
3.2.2 Marketing With a Data Warehouse
3.2.3 Targeting Customers
3.2.4 Churn Analysis
3.3 Data Warehouse Results
3.3.1 Case Study One
3.3.2 Case Study Two
Chapter IV
DATA WAREHOUSE IMPLEMENTATIONS BY LEADING CARRIERS
4.1 360° Communications
4.1.1 Data Warehouse Applications
4.1.2 Demographics
4.1.3 Billing
4.1.4 Marketing
4.1.5 Multidimensional Analysis
4.1.6 Data Warehouse Technology
4.1.6.1 Platforms
4.1.6.2 Tools
4.1.7 Comments
4.2 GTE
4.2.1 Data Warehouse Applications
4.2.2 GTE's Data Warehouse and Data Marts
4.2.3 Statistical Modeling
4.2.4 ChurnManager
4.2.5 Data Warehouse Technology
4.2.5.1 Platforms
4.2.5.2 Tools
4.3 MCI
4.3.1 Data warehouseMCI
4.3.1.1 Implementing warehouseMCI
4.3.1.2 Data Warehouse Technology
4.3.1.3 The warehouseMCI Infrastructure
4.3.1.4 Customer Data
4.3.1.5 Data Warehouse Benefits
4.4 Lessons Learned in Implementing A Data Warehouse
4.4.1 The Manager's Role
4.4.2 The Top-Down Model
Chapter V
DATA WAREHOUSE VENDOR PROFILES
5.1 Apertus Carleton
5.1.1 Data Warehouse Products
5.1.2 Partners
5.1.3 Major Contracts
5.1.4 Market Positioning
5.2 Arbor Software Corporation
5.2.1 Data Warehouse Products
5.2.2 Partners and Major Contracts
5.2.3 Market Positioning
5.3 Brio Technology, Inc.
5.3.1 Data Warehouse Products
5.3.1.1 Platform
5.3.2 Partners and Major Contracts
5.3.3 Market Positioning
5.4 Business Objects
5.4.1 Data Warehouse Products
5.4.2 Partners
5.4.3 Major Contracts
5.4.4 Market Positioning
5.5 CorVu Corporation
5.5.1 Data Warehouse Products
5.5.2 Partners and Major Contracts
5.5.3 Market Positioning
5.6 Digital Equipment Corporation
5.6.1 Data Warehouse Products
5.6.2 Partners
5.6.3 Major Contracts
5.6.4 Market Positioning
5.7 Gentia Software
5.7.1 Data Warehouse Products
5.7.1.1 Platform
5.7.2 Partners and Major Contracts
5.7.3 Market Positioning
5.8 Hewlett-Packard Company
5.8.1 Data Warehouse Products
5.8.2 Partners and Major Contracts
5.8.3 Market Positioning
5.9 Hummingbird Communications Limited
5.9.1 Data Warehouse Products
5.9.2 Partners and Major Contracts
5.9.3 Market Positioning
5.10 Hyperion Software Corporation
5.10.1 Data Warehouse Products
5.10.2 Partners
5.10.3 Major Contracts
5.10.4 Market Positioning
5.11 IBM Corporation
5.11.1 Data Warehouse Products
5.11.1.1 Platform
5.11.2 Partners
5.11.3 Major Contracts
5.11.4 Market Positioning
5.12 Informatica Corporation
5.12.1 Data Warehouse Products
5.12.1.1 Platform
5.12.2 Partners
5.12.3 Major Contracts
5.12.4 Market Positioning
5.13 Information Advantage, Inc.
5.13.1 Data Warehouse Products
5.13.1.1 Platform
5.13.2 Partners
5.13.3 Major Contracts
5.13.4 Market Positioning
5.14 Informix Software, Inc.
5.14.1 Data Warehouse Products
5.14.2 Partners
5.14.3 Major Contracts
5.14.4 Market Positioning
5.15 MicroStrategy, Inc.
5.15.1 Data Warehouse Products
5.15.1.1 Platform
5.15.2 Partners and Major Contracts
5.15.3 Market Positioning
5.16 NCR
5.16.1 Data Warehouse Products
5.16.2 Partners and Major Contracts
5.16.3 Market Positioning
5.17 Naviant Technology Solutions
5.17.1 Data Warehouse Products
5.17.2 Partners and Major Contracts
5.17.3.1 The Usage Data Warehouse Pilot Project
5.17.3 Market Positioning
5.18 Oracle Corporation
5.18.1 Data Warehouse Products
5.18.1 Partners
5.18.2 Major Contracts
5.18.3 Market Positioning
5.19 Pilot Software
5.19.1 Data Warehouse Products
5.19.2 Partners and Major Contracts
5.19.3 Market Positioning
5.20 Pine Cone Systems
5.20.1 Data Warehouse Products
5.20.1.1 Platform
5.20.2 Partners
5.20.3 Major Contracts
5.20.4 Market Positioning
5.21 Platinum Technology, Inc.
5.21.1 Data Warehouse Products
5.21.2 Partners
5.21.3 Major Contracts
5.21.4 Market Positioning
5.22 Prism Solutions, Inc.
5.22.1 Data Warehouse Products
5.22.2 Partners and Major Contracts
5.22.3 Market Positioning
5.23 Red Brick Systems, Inc.
5.23.1 Data Warehouse Products
5.23.2 Partners
5.23.3 Major Contracts
5.23.4 Market Positioning
5.24 SAS Institute, Inc.
5.24.1 Data Warehouse Products
5.24.2 Partners
5.24.3 Major Contracts
5.24.4 Market Positioning
5.25 Sagent Technology, Inc.
5.25.1 Data Warehouse Products
5.25.2 Partners
5.25.3 Major Contracts
5.25.4 Market Positioning
5.26 SearchSoftwareAmerica
5.26.1 Data Warehouse Products
5.26.2 Market Positioning
5.27 Sun Microsystems
5.27.1 Data Warehouse Products
5.27.2 Partners
5.27.3 Major Contracts
5.27.4 Market Positioning
5.28 Sybase, Inc.
5.28.1 Data Warehouse Products
5.28.1.1 Platform
5.28.2 Partners
5.28.3 Major Contracts
5.28.4 Market Positioning
5.29 Thinking Machines Corporation
5.29.1 Data Warehouse Products
5.29.2 Partners and Major Contracts
5.29.3 Market Positioning
5.30 Vality Technology, Inc.
5.30.1 Data Warehouse Products
5.30.2 Partners
5.30.3 Major Contracts
5.30.4 Market Positioning
Chapter VI
DATA WAREHOUSING MARKET FORECAST
6.1 Introduction
6.1.1 Types of Decision-Support Applications
6.2 Methodology
6.2.1 Market Segmentation
6.3 Global Forecasts
6.4 Data Warehouse Forecast
6.5 Data Mart Forecast
6.6 Data Mining Forecast
6.7 OLAP Forecast
Table of Figures
Chapter I
I-1 Worldwide Forecast for Total Telecom Data Warehouse
Market, 1998-2003 ($Millions)
I-2 Worldwide Forecast for Total Telecom Data Warehouse
Market, 1998-2003 ($Millions)
I-3 Global Data Warehouse Product Distribution, 1998 and
2003
Chapter II
II-1 Traditional Statistical Tools versus Data Warehouse
Tools
II-2 Data Warehouse Web-Based Architecture
II-3 Data Input Requirements
II-4 Typical Data Warehouse System Configuration
II-5 Steps in Building a Data Warehouse
Chapter III
III-1 Call Center Marketing and Sales Applications
Chapter IV
IV-1 ChurnManager Customer Retention Process
IV-2 ChurnManager Architecture
Chapter V
V-1 Apertus Carleton's Enterprise/Integrator
V-2 Informatica's PowerMart Suite
V-3 Informix's MetaCube
V-4 The Churn Model Power Curve
Chapter VI
VI-1 Worldwide Wireline and Wireless Telecom Service
Provider Revenue Forecast, 1998-2003 ($Millions)
VI-2 North American Service Provider Revenue Forecast by
Market Segment, 1998-2003 ($Millions)
VI-3 Worldwide Telecom Service Provider Wireline Revenue
Forecast, 1998-2003 ($Millions)
VI-4 Worldwide Telecom Service Provider Wireless Revenue
Forecast, 1998-2003 ($Millions)
VI-5 Worldwide Telecom Service Provider Revenue Forecast
by Region, 1998-2003 ($Millions)
VI-6 Worldwide Forecast for Total Telecom Data Warehouse
Market, 1998-2003 ($Millions)
VI-7 Worldwide Forecast for Total Telecom Data Warehouse
Market by Product Type, 1998-2003 ($Millions)
VI-8 Global Data Warehouse Product Distribution, 1998 and
2003
VI-9 Worldwide Telecom Data Warehouse Market Forecast by
Geographic Region, 1998-2003 ($Millions)
VI-10 North American Forecast for Total Telecom Data
Warehouse Market by Product Type, 1998-2003 ($Millions)
VI-11 Europe/Middle East/Africa Forecast for Total
Telecom Data Warehouse Market by Product Type, 1998-2003
($Millions)
VI-12 Asia/Pacific Forecast for Total Telecom Data
Warehouse Market by Product Type, 1998-2003 ($Millions)
VI-13 Latin America/Caribbean Forecast for Total Telecom
Data Warehouse Market by Product Type, 1998-2003
($Millions)
VI-14 North American Forecast for Telecom Data Warehouse
Systems by Market Segment, 1998-2003 ($Millions)
VI-15 Worldwide Wireline Forecast for Telecom Data
Warehouse Systems by Market Segment, 1998-2003
($Millions)
VI-16 Worldwide Wireless Forecast for Telecom Data
Warehouse Systems by Market Segment, 1998-2003
($Millions)
VI-17 Worldwide Telecom Data Warehouse System Market,
Share, 1998 and 2003
VI-18 North American Forecast for Telecom Data Mart
Systems, by Market Segment, 1998-2003 ($Millions)
VI-19 Wireline Forecast for Telecom Data Mart Systems, by
Market Segment, 1998-2003 ($Millions)
VI-20 Wireless Forecast for Telecom Data Mart Systems, by
Market Segment, 1998-2003 ($Millions)
VI-21 Worldwide Telecom Data Mart System Market Share,
1998 and 2003 193
VI-22 North American Forecast for Telecom Data Mining
Software, by Market Segment, 1998-2003 ($Millions)
VI-23 Wireline Forecast for Telecom Data Mining Software,
by Market Segment, 1998-2003 ($Millions)
VI-24 Wireless Forecast for Telecom Data Mining Software,
by Market Segment, 1998-2003 ($Millions)
VI-25 Worldwide Telecom Data Mining Software Market,
Share, 1998 and 2003
VI-26 North American Forecast for Telecom OLAP Software
by Market Segment, 1998-2003 ($Millions)
VI-27 Wireline Forecast for Telecom OLAP Software by
Market Segment, 1998-2003 ($Millions)
VI-28 Wireless Forecast for Telecom OLAP Software by
Market Segment, 1998-2003 ($Millions)
VI-29 Worldwide Telecom OLAP Software Market Share, 1998
and 2003
Table of Tables
Chatper VI
VI-1 Worldwide Telecom Service Provider Revenue Forecast
by Market Segment, 1998-2003 ($Millions)
VI-2 Descriptions of Provider Types
VI-3 Worldwide Forecast for Total Telecom Data Warehouse
Market by Product Type, 1998-2003 ($Millions)
VI-4 Worldwide Forecast for Total Telecom Data Warehouse
Market by Geographic Region, 1998-2003 ($Millions)
VI-5 Worldwide Forecast for Telecom Data Warehouse
Systems by Market Segment, 1998-2003 ($Millions)
VI-6 Worldwide Forecast for Telecom Data Mart Systems, by
Market Segment, 1998-2003 ($Millions)
VI-7 Worldwide Forecast for Telecom Data Mining Software,
by Market Segment, 1998-2003 ($Millions)
VI-8 Worldwide Forecast for Telecom OLAP Software by
Market Segment, 1998-2003 ($Millions)
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Telecom
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